Job Description & Skill Requirement:
Core Responsibilities
• Design and build advanced AI-driven systems utilizing LLMs (e.g., Azure OpenAI GPT
Models, Claude, Llama, Mistral, Gemini, and open-source models) for tasks such as text
understanding, generation, summarization, and contextual reasoning within engineering
workflows.
• Architect and deploy agentic pipelines (multi-agent systems, autonomous LLM agents,
chain-of-thought/reasoning systems) for process automation, decision support, and
engineering knowledge orchestration.
• Develop and implement Advanced Retrieval-Augmented Generation (RAG) solutions —
combining LLMs with vector databases, search engines, and enterprise knowledge sources
for high-fidelity document analysis and Q&A.
• End-to-End automation of complex human-in-the-loop processes by chaining LLMs, expert
systems, and external tools using orchestration frameworks (such as LangChain,
LlamaIndex, Haystack, CrewAI, etc.).
• Evaluate, select, and integrate modern and emerging AI tools, APIs, and infrastructure
(LLMOps, vector stores, document loaders, prompt management, agents frameworks, etc).
• Fine-tune, deploy, and monitor LLMs on private/in-house datasets to solve unique domain
challenges and maintain compliance/privacy.
• Stay current with the fast-evolving AI landscape (open weights, small/efficient models,
guardrails, synthetic data, evaluation techniques, multimodal models, etc.), and bring new
approaches into the organization.
Preferred:
• Experience optimizing for model cost, latency, reliability, and scaling in production.
• Understanding of privacy, security, and compliance in LLM/AI applications (PII scrubbers,
access controls, audit trails).
• Experience orchestrating multi-agent/agentic workflows (CrewAI, AutoGen, OpenAgents,
etc.).
• Familiarity with CI/CD for AI pipelines, containerization (Docker), and cloud AI services
(Azure ML, AWS Sagemaker, GCP Vertex).
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